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--- |
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tags: |
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- image-to-text |
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- image-captioning |
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license: apache-2.0 |
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language: |
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- zh |
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widget: |
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- src: https://datasets-server.huggingface.co/assets/Maciel/e-commerce-sample-images/--/Maciel--e-commerce-sample-images/train/2/image/image.jpg |
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example_title: 小耳钉 |
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- src: https://datasets-server.huggingface.co/assets/Maciel/e-commerce-sample-images/--/Maciel--e-commerce-sample-images/train/5/image/image.jpg |
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example_title: 短裙 |
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- src: https://datasets-server.huggingface.co/assets/Maciel/e-commerce-sample-images/--/Maciel--e-commerce-sample-images/train/7/image/image.jpg |
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example_title: 高跟鞋 |
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--- |
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### 功能介绍 |
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该模型功能主要是对图片生成文字描述。模型结构使用Encoder-Decoder结构，其中Encoder端使用BEiT模型，Decoder使用GPT模型。 |
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使用中文Muge数据集训练语料，训练5k步，最终验证集loss为0.3737，rouge1为20.419，rouge2为7.3553，rougeL为17.3753，rougeLsum为17.376。 |
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[Github项目地址](https://github.com/Macielyoung/Chinese-Image-Caption) |
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### 如何使用 |
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```python |
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from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer |
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from PIL import Image |
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pretrained = "Maciel/Muge-Image-Caption" |
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model = VisionEncoderDecoderModel.from_pretrained(pretrained) |
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feature_extractor = ViTFeatureExtractor.from_pretrained(pretrained) |
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tokenizer = AutoTokenizer.from_pretrained(pretrained) |
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image_path = "https://huggingface.co/Maciel/Muge-Image-Caption/blob/main/%E9%AB%98%E8%B7%9F%E9%9E%8B.jpg" |
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image = Image.open(image_path) |
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if image.mode != "RGB": |
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image = image.convert("RGB") |
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pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values |
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output_ids = model.generate(pixel_values, **gen_kwargs) |
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preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) |
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preds = [pred.strip() for pred in preds] |
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print(preds) |
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``` |
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